# Doubly Optimal Policy Evaluation for Reinforcement Learning

arXiv: https://arxiv.org/abs/2410.02226 · HTML: https://mlsanity.com/paper/2410.02226/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods.
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [RL theory and sample complexity](https://mlsanity.com/deeprl/rl-theory-and-sample-complexity/)

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/1806.10293/) (2018)
- [Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review](https://mlsanity.com/paper/1805.00909/) (2018)
- [Doubly Robust Off-policy Value Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1511.03722/) (2015)
- [Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1604.00923/) (2016)
- [DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections](https://mlsanity.com/paper/1906.04733/) (2019)
- [Batch Policy Learning under Constraints](https://mlsanity.com/paper/1903.08738/) (2019)
- [An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning](https://mlsanity.com/paper/1503.04269/) (2015)
- [Data-Efficient Policy Evaluation Through Behavior Policy Search](https://mlsanity.com/paper/1706.03469/) (2017)
- [The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise](https://mlsanity.com/paper/2401.07844/) (2024)
- [Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines](https://mlsanity.com/paper/1706.06643/) (2017)
- [ReVar: Strengthening Policy Evaluation via Reduced Variance Sampling](https://mlsanity.com/paper/2203.04510/) (2022)
- [Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning](https://mlsanity.com/paper/2111.14552/) (2021)
- [Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design](https://mlsanity.com/paper/2301.13734/) (2023)
- [Semi-analytical Industrial Cooling System Model for Reinforcement Learning](https://mlsanity.com/paper/2207.13131/) (2022)
- [Efficient Multi-Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/2408.08706/) (2024)

## Built on by (most cited first, 8 of 8)

- [Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning](https://mlsanity.com/paper/2410.05655/) (2024)
- [Offline Two-Player Zero-Sum Markov Games with KL Regularization](https://mlsanity.com/paper/2605.13025/) (2026)
- [Pessimism-Free Offline Learning in General-Sum Games via KL Regularization](https://mlsanity.com/paper/2605.00264/) (2026)
- [Robust Data-Collection Policy Learning for Low-Variance Online Policy Evaluation](https://mlsanity.com/paper/2608.24146/) (2026)
- [Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation](https://mlsanity.com/paper/2505.22492/) (2025)
- [Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning](https://mlsanity.com/paper/2602.01853/) (2026)
- [OR-Transformer: Scaling Real-Time Decision-Making to 1,000 Items](https://mlsanity.com/paper/2609.01933/) (2026)
- [Pessimistic Minimax Learning for Public-Private Information Games under Unilateral Coverage](https://mlsanity.com/paper/2610.04997/) (2026)

## Abstract (arXiv)

Policy evaluation estimates the performance of a policy by (1) collecting data from the environment and (2) processing raw data into a meaningful estimate. Due to the sequential nature of reinforcement learning, any improper data-collecting policy or data-processing method substantially deteriorates the variance of evaluation results over long time steps. Thus, policy evaluation often suffers from large variance and requires massive data to achieve the desired accuracy. In this work, we design an optimal combination of data-collecting policy and data-processing baseline. Theoretically, we prove our doubly optimal policy evaluation method is unbiased and guaranteed to have lower variance than previously best-performing methods. Empirically, compared with previous works, we show our method reduces variance substantially and achieves superior empirical performance.